COMPANY

What Came First, Cognitive or Business Intelligence?

Companies often hinder progress because of the classic chicken and egg dilemma, which prevents them from seeing a clear or simple path to get started. In nascent areas like cognitive, this delay tactic is even more common. Feeling their way through what it means to be cognitive, people will argue that you need to already have a foundation of business intelligence, and well-understood data in order to take on a cognitive or artificial intelligence initiative. Yet, as many times as it is written and spoken, people still get lost in the soup of their business and forget that they need to back into technology solutions by starting with a goal, and outcome, an opportunity.

Rather than deliberating over what should come first, cognitive capabilities or business intelligence, companies need to explore both by devising a digital-transformation strategy.

Cognitive Uncovers Strategic Business Goals

Stakeholders come to us with an understanding of their business’ problems and potential related solutions, but often we find that their perspective – while informed by their own experience – hasn’t been validated by users or the market.

When a client jumps straight to solution building, it’s our responsibility to flip the script.

Most often, clients ask:

How do we build it?

But we want them asking:

What is it? And, why do you need it?

Taking a business out of the solutions space encourages members to think critically about what the outcomes of this improvement process should be. Then, we can collectively engineer a digital-ready solution with a clear roadmap and specific cognitive technologies for achieving these outcomes.

Strategic Planning Requires an Outside-In Approach

To prioritize cognitive moving forward, businesses must not only explore an inside-out business intelligence strategy but also in parallel take an outside-in approach. Consider the image below to see this comparison in action:

When using an inside-out approach, businesses develop proof of concepts (POCs) based on years of data that’s difficult to aggregate. For example, an insurance provider may sift through a decade’s worth of historical claims data and build out new services based on insights derived from that data pool. The provider may then layer on user interface/interaction improvements and hope the process achieves some of its desired outcomes.

But this blind faith doesn’t have to live in isolation. By starting outside-in, user research informs how and when businesses invest in solutions that actually meet real business needs. In fact, pursuing an outside-in cognitive strategy allows teams to work parallel business-intelligence pathways at once.

Insights can come from customers, too. For instance, if customers are experiencing consistent confusion around bills and payments, the provider could identify this area as ripe for digital intervention. Could cognitive capabilities help predict information to make payment simpler, with less friction?

Once you’ve identified a user need and its desired outcomes, solutioning develops requirements for the new system and drives understanding of the data you need to make this system work. By pursuing both pathways simultaneously, you can identify gaps between the data you have and the data sourced from users, or third party data sources to enrich your proprietary data. Or, you could alter your own proprietary data collection practices to fill this void with new insights.

No matter what you choose, your business cannot develop cognitive insights that leverage all data in your system if you don’t pursue multiple pathways together.

Cognitive Doesn’t Lead to a One-Size-Fits-All Business Roadmap

There’s an urgency to welcome new technologies and cognitive is trending similarly. Take chat bots. Businesses already equipped with digital foundations are looking to embrace artificial intelligence. But is this because it’s trendy, or because there’s a real business case for it?

Likewise, cognitive can mean many things. Do you want sentiment analysis to gauge call center language, or is machine learning focused on predictive capabilities a better fit for navigating users to a next action? Maybe you’re in the market for natural language processing because you have high miss rates on your clients failing to find what they need through a search.

Think back to the rise of mobile – we can see the inherent faults of trend-based investment. As mobile popularized, every business built an app. Many did so without first thinking through the goals and advantages of this technology, or how users would interact with the new channel. Uniformed investing resulted in introductory mobile solutions with little value.

As with any other project or investment, new technologies must prove their ROI by either creating efficiencies or different experiences for users. Businesses are not excused from the rigors of weighing a potential cognitive investment just because it’s the coolest new thing. Instead, decision makers must work outside-in to understand the user problem they hope to fix and do the work of making their solutions valuable. Bad first impressions linger even for solutions in their infancy, meaning your business has to get it right the first time.

To make it much simpler than a chicken and egg riddle, here are the two main rules of cognitive investment:

Rule No.1: Work outside-in for user and market driven insights in parallel with inside-out exploration of data

Rule No.2: Apply all of the typical tenets of investment when selecting how to take advantage of cognitive capabilities

With these guiding principles, every business can establish cognitive efficiencies that drive business results for real users, when the time is right. And whenever that time may be, PointSource is here to help.

Click here to learn more about how our team can craft a digital strategy to introduce cognitive improvements.